Advanced Dairy Farm Monitoring and Intelligence System

EYantra Herd Link is a comprehensive, edge-to-cloud ecosystem designed to modernize livestock tracking, behavioral analysis, and proactive health monitoring for dairy farms. By integrating custom edge hardware, on-device machine learning, high-performance local processing, and robust mobile applications, we provide actionable, real-time insights into herd health, environmental conditions, and individual animal well-being.

Our solution operates cohesively across four primary pillars, extending from the physical sensor level in the barn to the end-user mobile interface:

Repository: Collar

• Technology Stack: C++, PlatformIO, FreeRTOS, ESP32-C3, KiCad
• Overview: The HerdLink Collar V1 is a low-power, edge-computing wearable device designed for continuous livestock monitoring.
• Key Features:
  • Behavioral Inference: Utilizes a 6-axis IMU (MPU6050) to calculate activity jerk and variance, enabling the categorization of behaviors such as grazing, ruminating, walking, and resting.
  • Health & Environment Sensors: Integrates a Dallas DS18B20 sensor for precise body temperature measurements and an AHT20 sensor for ambient temperature and humidity tracking.
  • Audio Edge Processing: Captures ambient and respiratory audio via an I2S microphone, performing on-device Fast Fourier Transform (FFT) to extract Mel-frequency features (MFCCs) while preserving bandwidth and privacy.
  • Power Optimization: Leverages FreeRTOS tasks, hardware interrupts, and ESP32 Light Sleep modes to maximize battery life while transmitting UDP telemetry over local WiFi networks.

Repository: node

• Technology Stack: Rust, Tokio, Axum, SQLite, Supabase
• Overview: Serving as the brain of the operation, this Rust-based ingestion server runs on local edge hardware (such as a Raspberry Pi 5). It receives high-frequency telemetry from the smart collars and processes complex behavioral analytics in real-time.
• Key Features:
  • Real-Time Analytics: Applies physics-based heuristics to classify cow states instantly, utilizing state smoothing (majority voting) to eliminate transient noise.
  • Herd Intelligence: Implements collaborative filtering to compare individual data against the herd average. It dynamically adjusts thresholds for fever and detects anomalies like lameness or estrus based on rolling behavioral baselines.
  • Hybrid Cloud Architecture: Functions primarily as an offline-first Edge Server, processing data locally and buffering it during internet outages. It asynchronously synchronizes aggregated insights and daily statistics to a central Supabase database for global remote access.
  • Smart Storage Management: Automatically prunes bulky raw data when disk usage reaches critical thresholds, ensuring continuous operation without manual intervention.

Repository: app

• Technology Stack: Kotlin, Jetpack Compose, Room, Hilt, Firebase, WorkManager
• Overview: A robust, fully native Android application tailored for both farm operators and veterinarians. It provides an intuitive interface for managing herd inventory, reviewing health history, and responding to critical alerts.
• Key Features:
  • Dual Portals: Offers a Farmer Portal featuring real-time dashboards, GPS herd mapping, and actionable alert feeds, alongside a distinct Veterinarian Portal for managing clinical referrals and diagnostic reporting.
  • Offline-First Resilience: Built on an MVVM architecture using Room database and background synchronization via WorkManager, ensuring full operability in remote agricultural environments with intermittent connectivity.
  • Health Engine Integration: Computes composite health scores, evaluates Temperature-Humidity Index (THI) stress, and generates human-readable health assessments derived from raw telemetry data.
  • On-Device Machine Learning: Integrates the breed classification pipeline directly into the application, enabling instant, offline cattle breed identification via the device camera.

Repository: Breed-Classifier

• Technology Stack: Python, PyTorch, timm (EfficientNet-B0), Ultralytics (YOLOv8-nano), ONNX
• Overview: A specialized deep learning pipeline engineered for the fine-grained visual classification of 41 bovine breeds, including indigenous Indian breeds and international dairy cattle.
• Key Features:
  • Two-Stage Inference Cascade: Utilizes YOLOv8-nano for precise bovine object detection and region-of-interest extraction, followed by an EfficientNet-B0 classifier for accurate breed identification.
  • Robust Transfer Learning: Employs a two-phase transfer learning protocol with discriminative learning rates, extensive offline data augmentation, and advanced regularization techniques (Mixup, CutMix) to combat class imbalance.
  • Cross-Platform Deployment: The fully trained model is rigorously validated and exported to the ONNX interchange format, facilitating highly optimized, low-latency execution on edge devices and mobile platforms without cloud dependencies.

The EYantra Herd Link ecosystem prioritizes data security and operational isolation. Each Central Intelligence Hub generates a unique farm identifier and utilizes secure API authentication mechanisms. By extracting mathematical audio features (MFCCs) instead of transmitting raw audio, the system inherently protects operational privacy while enabling advanced respiratory health monitoring.

We are dedicated to advancing agricultural technology through transparent, rigorously engineered systems. We welcome academic collaborations, external contributions, and integrations with our platform. Please review the specific technical documentation, architecture diagrams, and contribution guidelines located within each respective repository.

Engineering Intelligence for Modern Agriculture.